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CLASSIFYING PEROXIREDOXIN SUBGROUPS AND IDENTIFYING DISCRIMINATING MOTIFS VIA MACHINE LEARNING

Electronic Theses and Dissertations

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abstract
Accurate and automated functional annotation is a pressing open problem, with functional characterizations lagging far behind the exponential growth in biological sequence databases. In this thesis, I present our recent development of machine learning methods for high-throughput, accurate, sequence-based functional annotation. Chapter 1 describes the biological and computational background of this study. Chapter 2defines the specific problem we try to solve. Chapter 3 demonstrates that our 3mer-SVM, that accurately classifies Peroxiredoxin subgroups, can provide meaningful additional insight into the functional conserved sites in Peroxiredoxin protein. Moreover, in Chapter 4, we propose a two-round learning algorithm that can capture gapped-kmer features in sequences and lead to more accurate classifications than the kmer-SVM approach. We illustrate this learning algorithm can be useful as a de novo motif finder for uncovering discriminating motifs among sequences associated with particular activities and functions. With a brief discussion on the advantage and limitations on our kmer-based sequence classification and \textit{de novo} motif identification, in Chapter 5, we propose several potential applications for future directions.
subject
contributor
Xiao, Jiajie (author)
Turkett, William H (committee chair)
John, David (committee member)
Ballard, Grey (committee member)
Pease, James (committee member)
date
2018-05-24T08:36:02Z (accessioned)
2019-05-23T08:30:11Z (available)
2018 (issued)
degree
Computer Science (discipline)
embargo
2019-05-23 (terms)
identifier
http://hdl.handle.net/10339/90706 (uri)
language
en (iso)
publisher
Wake Forest University
title
CLASSIFYING PEROXIREDOXIN SUBGROUPS AND IDENTIFYING DISCRIMINATING MOTIFS VIA MACHINE LEARNING
type
Thesis

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